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Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM

Overview of attention for article published in BMC Bioinformatics, November 2014
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Mentioned by

twitter
3 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
24 Mendeley
citeulike
2 CiteULike
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Title
Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVM
Published in
BMC Bioinformatics, November 2014
DOI 10.1186/1471-2105-15-340
Pubmed ID
Authors

Liqi Li, Sanjiu Yu, Weidong Xiao, Yongsheng Li, Lan Huang, Xiaoqi Zheng, Shiwen Zhou, Hua Yang

Abstract

Identification of the recombination hot/cold spots is critical for understanding the mechanism of recombination as well as the genome evolution process. However, experimental identification of recombination spots is both time-consuming and costly. Developing an accurate and automated method for reliably and quickly identifying recombination spots is thus urgently needed.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 25%
Student > Ph. D. Student 4 17%
Student > Bachelor 2 8%
Student > Doctoral Student 2 8%
Student > Master 2 8%
Other 4 17%
Unknown 4 17%
Readers by discipline Count As %
Computer Science 5 21%
Biochemistry, Genetics and Molecular Biology 4 17%
Agricultural and Biological Sciences 3 13%
Medicine and Dentistry 2 8%
Business, Management and Accounting 1 4%
Other 3 13%
Unknown 6 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 November 2014.
All research outputs
#14,312,519
of 21,321,365 outputs
Outputs from BMC Bioinformatics
#5,100
of 6,933 outputs
Outputs of similar age
#201,768
of 346,251 outputs
Outputs of similar age from BMC Bioinformatics
#323
of 467 outputs
Altmetric has tracked 21,321,365 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,933 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,251 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 467 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.